Synthesizing apparatus, synthesizing method and program

ABSTRACT

A synthesizing apparatus comprises: an input part that inputs a plurality of feature point sets that are respectively extracted by a plurality of methods from an input image having a curved stripes pattern formed by ridges; and a synthesizing part that synthesizes the plurality of feature point sets by executing a logical operation on the plurality of feature point sets. The synthesizing part can execute a logical OR operation on the plurality of feature point sets. The synthesizing part can also execute a logical AND operation on the plurality of feature point sets.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 16/343,526 filed on Apr. 19, 2019, which is aNational Stage Entry of international application PCT/JP2017/038068,filed Oct. 20, 2017, which claims the benefit of priority from JapanesePatent Application 2016-206879 filed on Oct. 21, 2016, the disclosuresof all of which are incorporated in their entirety by reference herein.

TECHNICAL FIELD

The present invention relates to a synthesizing apparatus, asynthesizing method and a program. In particular the invention relatesto a synthesizing apparatus, a synthesizing method and a program whichhandle a set including feature points extracted from an image having acurved stripes pattern formed by ridges.

BACKGROUND

A finger print or a palm print having a curved stripes pattern formed bymany ridges has been used as a means of identifying a person for a longtime now. In particular, matching a finger print left behind at thescene of a crime is used as an effective investigation means. Fingerprint matching systems using a computer have been installed in manypolice agencies. By comparing respective feature points of fingerprintsleft behind that are harvested at the scene of a crime with fingerprintimages recorded in a database, a person corresponding to the left-behindfingerprint is identified.

In the feature points used in fingerprint matching, end points andbranch points of fingerprint ridges are often used. For example, featurepoint matching using end points and branch points of fingerprint ridgesare used, as disclosed in “4.3 Minutiae-Based Methods” of Non-PatentLiterature (NPL) 1.

Patent Literature (PTL) 1 discloses technology for providing afingerprint image processing apparatus that can extract a fingerprintimage to be a target for matching with good accuracy. Patent Literature1 discloses characterizing the direction of feature points extractedfrom a fingerprint image, according to rotational direction with respectto a reference point.

PTL 1

-   Japanese Patent Kokai Publication No. JP2015-228070A

Non Patent Literature NPL1

-   D. Maltoni, “Handbook of Fingerprint Recognition”, Springer, 2003

SUMMARY

It is to be noted that the respective disclosures of the abovementionedcited technical literature are incorporated herein by reference thereto.The following analysis is given according to the present inventors.

There is a wide range of usage of fingerprint images, such as inindividual authentication in entry and exit management, and criminalidentification in a criminal investigation. In using these fingerprintimages, by extracting feature points from the fingerprint images andcomparing the extracted feature points with feature points extractedfrom images stored in a database or the like, there are common pointswith regard to authentication and individual identification.

However, in fingerprint image usage (usage case), requirements differwith respect to feature points extracted from the fingerprint images(more precisely, a set or feature vector formed from a plurality offeature points). For example, in a case of using a left-behindfingerprint in identifying a suspect in a criminal investigation,completeness in feature points is required, in order that a suspect'sfingerprints are not lost. That is, even with feature points consideredto be of low quality, it is desirable to have completeness inextraction, and to compare with the information of the database.

Meanwhile, in a case of being used for individual authentication or thelike, in order to reduce misjudgments (rejecting the person himself,accepting someone else), accuracy is required in feature pointsextracted from fingerprint images obtained by a scanner apparatus or thelike. That is, along with feature points that are influenced by theenvironment when obtaining a fingerprint being excluded in advance andrecorded in a database, it is desirable that such feature points of lowquality also be excluded when matching.

It is to be noted that it is preferable for there to be a feature pointextraction method (feature point extraction algorithm) enabling completeextraction of high quality feature points, but each feature pointextraction method has a particular character, and in actuality nouniversal algorithm exists.

It is an object of the present invention to provide a synthesizingapparatus, a synthesizing method and a program to extract feature pointssuitable for usage application.

According to a first aspect of the present invention and disclosure, asynthesizing apparatus includes: an input part that inputs a pluralityof feature point sets respectively extracted by a plurality of methodsfrom an input image having a curved stripes pattern formed by ridges;and a synthesizing part that synthesizes the plurality of feature pointsets by executing a logical operation on the plurality of feature pointsets.

According to a second aspect of the present invention and disclosure asynthesizing method includes: inputting a plurality of feature pointsets extracted by a plurality of methods from an input image having acurved stripes pattern formed by ridges; and synthesizing the pluralityof feature point sets by executing a logical operation on the pluralityof feature point sets.

According to a third aspect of the present invention and disclosure aprogram that causes a computer to execute processing includes: inputtinga plurality of feature point sets extracted by a plurality of methodsfrom an input image having a curved stripes pattern formed by ridges;and synthesizing the plurality of feature point sets by executing alogical operation on the plurality of feature point sets.

It is to be noted that this program may be recorded on acomputer-readable storage medium. The storage medium may benon-transient such as semiconductor memory, a hard disk, a magneticrecording medium, an optical recording medium or the like. The presentinvention may be embodied as a computer program product.

According to the present invention and respective viewpoints of thedisclosure, a synthesizing apparatus, a synthesizing method and aprogram are provided that contribute to extracting feature pointssuitable for usage application.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an outline of an example embodiment.

FIG. 2 is a diagram illustrating an example of a configuration of afeature point extraction system according to a first example embodiment.

FIG. 3 is a diagram illustrating an example of a hardware configurationof a synthesizing apparatus according to the first example embodiment.

FIG. 4 is a diagram illustrating an example of a processingconfiguration of a first feature point extraction apparatus.

FIG. 5 is a diagram illustrating operations of a central line extractionpart.

FIGS. 6A-6C are diagrams illustrating operations of a feature pointextraction part.

FIG. 7 is a diagram illustrating an example of a processingconfiguration of a synthesizing apparatus.

FIGS. 8A-8C are diagrams illustrating operations of a synthesizing partin the synthesizing apparatus.

FIGS. 9A and 9B are diagrams illustrating operations of a synthesizingpart in the synthesizing apparatus.

FIG. 10 is a flow chart illustrating an example of operations when thesynthesizing part synthesizes 2 feature point sets by a logical ORoperation.

FIGS. 11A-11C are diagrams illustrating operations of a synthesizingpart in the synthesizing apparatus.

FIGS. 12A and 12B are diagrams illustrating operations of a synthesizingpart in the synthesizing apparatus.

FIG. 13 is a flow chart illustrating an example of operations when thesynthesizing part synthesizes 2 feature point sets by a logical ANDoperation.

FIGS. 14A and 14B are diagrams illustrating operations of a synthesizingpart in the synthesizing apparatus.

FIG. 15 is a sequence diagram illustrating an example of operations of afeature point extraction system according to the first exampleembodiment.

FIG. 16 is a diagram illustrating an example applying a plurality ofalgorithms to a fingerprint image.

FIG. 17 is a diagram illustrating an example of a configuration of afeature point extraction system according to a second exampleembodiment.

FIG. 18 is a flowchart illustrating an example of operations of thesynthesizing part.

FIG. 19 is a diagram illustrating an example of a configuration of afeature point extraction system according to a third example embodiment.

FIG. 20 is a diagram illustrating an example of a processingconfiguration of a synthesizing apparatus according to the third exampleembodiment.

FIGS. 21A and 21B are diagrams illustrating the number of ridges betweenfeature points.

PREFERRED MODES

First, a description is given concerning an outline of an exampleembodiment. It is to be noted that reference symbols in the drawingsattached to this outline are added to respective elements forconvenience as examples in order to aid understanding, and there is nointention to limit the invention in any way.

A synthesizing apparatus 100 according to an example embodiment isprovided with: an input part 101 that inputs a plurality of featurepoint sets that are respectively extracted using a plurality of methodsfrom an input image having a pattern of curved stripes (termed “curvedstripes pattern” herein) formed by ridges, and a synthesizing part 102that synthesizes the plurality of feature point sets by executinglogical operations on the plurality of feature point sets.

The feature point sets inputted to the synthesizing apparatus 100 areextracted by different methods (algorithms). In actuality there arestrengths and weaknesses in each method; specifically, there arealgorithms specialized for fingerprint images having particularfeatures. In the synthesizing apparatus 100 according to the exampleembodiment, with regard to the feature point sets extracted by therespective algorithms, a logical operation (for example, logical ORoperation, logical AND operation) is executed, and a plurality offeature point sets are synthesized into 1 feature point set. On such anoccasion, by suitably selecting a logical operation executed by thesynthesizing apparatus 100, it is possible to extract a feature pointset that realizes a purpose. For example, in a case of selecting,without omission, feature points extracted by a plurality of algorithms,a logical OR operation may be executed. Or, in a case where it isdesired that only high quality feature points extracted by the pluralityof algorithms remain, a logical AND operation may be executed.

A more detailed description is given concerning specific exampleembodiments below, making reference to the drawings. It is to be notedthat in each of the example embodiments, the same symbols are attachedto the same configuration elements and descriptions thereof are omitted.Connection lines between blocks in respective diagrams may bebidirectional or unidirectional. Unidirectional arrows schematicallyshow flow of main signals (data), but do not exclude bidirectionality.

First Example Embodiment

A more detailed description is given concerning a first exampleembodiment, using the drawings.

FIG. 2 is a diagram illustrating an example of a configuration of afeature point extraction system according to the first exampleembodiment. Referring to FIG. 2 , the feature point extraction system isconfigured to include a first feature point extraction apparatus 10, asecond feature point extraction apparatus 20, and a synthesizingapparatus 30.

Fingerprint images are inputted to each of the first feature pointextraction apparatus 10 and the second feature point extractionapparatus 20. Each of the first feature point extraction apparatus 10and the second feature point extraction apparatus 20 extract featurepoints from fingerprint images inputted according to methods(algorithms) that differ from one another.

A set of feature points extracted by the first feature point extractionapparatus 10 is denoted “first feature point set”. Similarly, a set offeature points extracted by the second feature point extractionapparatus 20 is denoted “second feature point set”.

The synthesizing apparatus 30 receives the first and the second featurepoint sets as input. An operator of the feature point extraction systeminputs a “synthesis mode instruction” to the synthesizing apparatus 30.The synthesizing apparatus 30 synthesizes the first and second featurepoint sets according to the synthesis mode instruction, and outputs thefeature point sets as feature amount characterizing the fingerprintimages inputted to the system. That is, the synthesizing apparatus 30 isan apparatus that synthesizes two or more feature point sets, andgenerates one feature point set.

<Hardware Configuration>

Next a description is given of a hardware configuration of various typesof apparatus configuring the feature point extraction system accordingto the first example embodiment.

FIG. 3 is a block diagram illustrating an example of a hardwareconfiguration of the synthesizing apparatus 30 according to the firstexample embodiment.

The synthesizing apparatus 30 is realized by a so-called informationprocessing apparatus (computer), and, for example, is provided with aconfiguration exemplified in FIG. 3 . For example, the synthesizingapparatus 30 is provided with a CPU (Central Processing Unit) 11, amemory 12, an input-output interface 13 and an NIC (Network InterfaceCard) 14 that is a communication means, connected together by aninternal bus.

However, the configuration illustrated in FIG. 3 is not intended to belimited to the hardware configuration of the synthesizing apparatus 30.The synthesizing apparatus 30 may include hardware not shown in thedrawings, and need not be provided with the input output interface 13 orthe NIC 14, in accordance with requirements. The number of CPUs includedin the synthesizing apparatus 30 is not limited to the example shown inFIG. 3 , and for example, a plurality of CPUs may be included in thesynthesizing apparatus 30.

The memory 12 may be RAM (Random Access Memory), ROM (Read Only Memory),or an auxiliary storage apparatus (hard disk etc.).

The input-output interface 13 is a means that forms an interface for adisplay apparatus or input apparatus not shown in the drawings. Thedisplay apparatus is, for example, a liquid crystal display or the like.The input apparatus is, for example, an apparatus that receives a useroperation such as that of a keyboard, a mouse, or the like. The inputapparatus includes an external storage apparatus such as a USB(Universal Serial Bus) memory or the like.

Functionality of the synthesizing apparatus 30 is realized by varioustypes of processing module described later. The processing modules inquestion, for example, are realized by the CPU 11 executing a programstored in the memory 12. The program may be downloaded via a network, ormay be updated using a storage medium that stores the program.Furthermore, the abovementioned processing modules may be realized by asemiconductor chip. That is, it is sufficient to have a means thatexecutes functions performed by the abovementioned processing modules,by some type of hardware and/or software.

It is to be noted that since hardware configurations of the firstfeature point extraction apparatus or second feature point extractionapparatus may be similar to the synthesizing apparatus 30, detaileddescriptions are omitted.

Continuing, a description is given concerning a processing configuration(processing module) of respective apparatuses.

<First Feature Point Extraction Apparatus>

FIG. 4 is a diagram illustrating an example of a processingconfiguration of the first feature point extraction apparatus 10.Referring to FIG. 4 , the first feature point extraction apparatus 10 isconfigured to include an input part 201, a central line extraction part202, a feature point extraction part 203, an output part 204, and astorage part 205. It is to be noted that respective parts of the inputpart 201 and the like are configured to enable transfer of data betweenthem, and access is possible to data stored in the storage part 205.

The input part 201 is a means for inputting data related to afingerprint image from outside (image having a curved stripes patternformed by ridges). For example, the input part 201 takes in digital data(image files) of fingerprint images stored in an external storage mediumsuch as a USB memory or the like, and delivers the data to the centralline extraction part 202. The input part 201 may input data related tothe fingerprint image via a network. Or, rather than a configuration forinputting a fingerprint image digitized by a scanner or the like, thedigitized fingerprint image may be obtained by installing a scannerfunction in the input part 201. It is to be noted that in a case ofobtaining the fingerprint image by a scanner, the first feature pointextraction apparatus 10 provides the image in question to the secondfeature point extraction apparatus 20.

Standards exist for the fingerprint image. Specifically, there existsANSI/NIST-ITL-1-2000 Data Format for the Interchange of Fingerprint,Facial, & Scar Mark & Tattoo (SMT) Information, standardized by the USNational Institute of Standards and Technology. It is preferable thatthe input part 201 handles fingerprint images digitized based on theabovementioned standard (for example, fingerprint images of 500 dpiresolution).

The central line extraction part 202 is a means for extracting a centralline from a fingerprint image that has been obtained. It is to be notedthat the central line extraction part 202 may use a central lineextraction method disclosed in “3 Fingerprint Analysis andRepresentation” of Non-Patent Literature 1. Therefore, while a detaileddescription regarding extraction of a central line is omitted, insummary the central line extraction part 202 extracts the central lineaccording to the following procedure.

The central line extraction part 202 extracts the directions of ridgesof the fingerprint image. The central line extraction part 202highlights each ridge in the direction of the ridge in question, andgenerates a binary image. The central line extraction part 202, byhaving the binary image as the central line, extracts the central linedata (central line image). For example, the central line extraction part202 generates a central line image as shown in FIG. 5 .

The feature point extraction part 203 is a means for extracting featurepoints from the central line image. Information extracted by the featurepoint extraction part 203 includes position of feature point, type offeature point (branch point, end point), and direction characterizingthe feature point in question according to direction (denoted below asfeature point direction).

The feature point extraction part 203 extracts, as feature points, abranch point and end point of the central line, from the central lineimage generated by the central line extraction part 202. It is to benoted that in the process of extracting feature points from the centralline image, it is possible to use a feature point extraction methoddisclosed in “3 Fingerprint Analysis and Representation” of Non-PatentLiterature 1. Thus, a detailed description related to feature pointextraction is omitted.

For example, the feature point extraction part 203 extracts branchpoints 211, 212, or end points 221, 222, as feature points as shown inFIG. 5 , and records position and type (branch point, end point) of eachfeature point in the storage part 205. It is to be noted that in FIG. 5and following diagrams, a branch point is denoted as a white square, andan end point is denoted as a white circle. Feature points (branch point,end point) illustrated in respective diagrams including FIG. 5 areconfigured for convenience in descriptions, and this does notcomprehensively indicate correct feature points to be extracted fromrespective diagrams.

When extraction of feature points is completed, the feature pointextraction part 203 calculates feature point direction related to eachfeature point. For example, if the extracted feature point is a branchpoint, the feature point extraction part 203 determines, as the featurepoint direction, a direction bisecting the smallest internal angledetermined from three central lines forming a feature point. Forexample, the feature point direction 232 of the branch point 231 shownin FIG. 6A is calculated.

If the extracted feature point is an end point, the feature pointextraction part 203 traces a fixed distance on a central line formingthe end point and calculates a terminal point. The feature pointextraction part 203 calculates the direction linking the end point andthe terminal point as a feature point direction of the end point. Forexample, the feature point direction 242 of the end point 241 shown inFIG. 6B is calculated.

It is to be noted that the feature point direction, as shown in FIG. 6C,is denoted as a feature amount using an angle θ formed by an X axis in a2-dimensional coordinate system within a fingerprint image, and astraight line in the feature point direction.

The feature point extraction part 203 delivers information related tothe extracted feature point (feature point position, type, feature pointdirection) to the output part 204.

The output part 204 outputs information related to the feature pointsextracted by the feature point extraction part 203 to the synthesizingapparatus 30 as a first feature point set. It is to be noted that anymode is possible for data transfer between the first feature pointextraction apparatus 10 and the synthesizing apparatus 30. For example,data related to the first feature point set may be inputted to thesynthesizing apparatus 30 via a network. Or, data related to the firstfeature point set may be inputted to the synthesizing apparatus 30 usinga USB memory or the like. The output part 204 may output not onlyfeature points (first feature point set) extracted from the fingerprintimage, but also in combination with fingerprint images to thesynthesizing apparatus 30.

<Second Feature Point Extraction Apparatus>

It is to be noted that since processing configuration related to thesecond feature point extraction apparatus 20 may be similar to the firstfeature point extraction apparatus 10, a detailed description isomitted. A point of difference between the first feature pointextraction apparatus 10 and the second feature point extractionapparatus 20, for example, may be a difference in algorithms related toextraction of feature points (operation of central line extraction part202, feature point extraction part 203).

For example, in a case where a blank space of several dots is present ona ridge in the fingerprint image, a determination judging that the blankspace is an interruption of the ridge, or judging that the blank spaceis noise and the ridge is not interrupted, differs between the twoapparatuses. Since respective feature point extraction algorithms of thefirst feature point extraction apparatus 10 and the second feature pointextraction apparatus 20 are different, the feature point sets outputtedfrom the respective apparatuses are also often different. However,according to the quality of the fingerprint image and the algorithmused, the results outputted by the two feature point extractionapparatuses may be the same.

<Synthesizing Apparatus>

FIG. 7 is a diagram illustrating an example of a processingconfiguration of the synthesizing apparatus 30. Referring to FIG. 7 ,the synthesizing apparatus 30 is configured to include an input part301, a synthesizing part 302, an output part 303 and a storage part 304.

The input part 301 is a means for inputting a plurality of feature pointsets extracted according to respective methods (feature point extractionalgorithms) from inputted images (fingerprint images) having a curvedstripes pattern formed by ridges. Specifically, the input part 301inputs two feature point sets (first and second feature point sets) anda synthesis mode instruction. The input part 301 delivers the obtainedtwo feature point sets and the synthesis mode instruction to thesynthesizing part 302.

The synthesizing part 302 is a means for synthesizing a plurality offeature point sets by executing a logical operation on the pluralfeature point sets. The synthesizing part 302 may execute various typesof logical operation. The synthesizing part 302 executes a logicaloperation specified by the synthesis mode instruction from among plurallogical operations. In the first example embodiment, the synthesizingpart 302 executes any of a logical OR operation or a logical ANDoperation on the two feature point sets.

For example, when a feature point is extracted by the first featurepoint extraction apparatus 10 from the fingerprint image shown in FIG.8A, a result as in FIG. 8B is obtained. In FIG. 8B, 4 feature points 251to 254 are extracted. Similarly, when a feature point is extracted bythe second feature point extraction apparatus 20 from the fingerprintimage shown in FIG. 8A, a result as in FIG. 8C is obtained. In FIG. 8C,4 feature points 261 to 264 are extracted.

The respective extraction results of FIGS. 8B and 8C are outputted tothe synthesizing apparatus 30 as first and second feature point sets.FIG. 9A shows an example of a feature point set outputted by the firstfeature point extraction apparatus 10, and FIG. 9B shows an example of afeature point set outputted by the second feature point extractionapparatus 20.

Referring to FIGS. 8A-8C and FIGS. 9A and 9B, it is understood thatfeature point 252 and feature point 262 are feature points extracted atthe same coordinate position, and types thereof are the same at branchpoints. Also, it is understood that feature point 253 and feature point263 are feature points extracted at the same coordinate position, andtypes thereof differ.

The synthesizing part 302 executes a logical operation (logical ORoperation or logical AND operation) on a plurality of feature point setsas shown in FIGS. 9A and 9B, and synthesizes two sets.

Next, a description is given concerning operations of the synthesizingpart 302 in a case where the synthesis mode instruction indicates alogical OR operation of the feature point sets.

FIG. 10 is a flow chart showing an example of operations when thesynthesizing part 30 synthesizes two feature point sets by a logical ORoperation.

The synthesizing part 302 extracts feature points belonging to onefeature point set, with coordinates corresponding to another featurepoint set not existing (with non-matching coordinates) (step S101). Itis to be noted that coordinates of the two feature points matchingindicates that when differences between the two coordinates arecalculated, it is within a prescribed range.

In the examples of FIGS. 8A-8C and FIGS. 9A and 9B, when step S101 isexecuted, feature point 251 and feature point 254 are extracted from thefirst feature point set, and feature point 261 and feature point 264 areextracted from the second feature point set. The extracted featurepoints are stored in the storage part 304 as elements (feature points)belonging to feature point sets after synthesis.

By the synthesizing part 302 executing processing of step S101, featurepoints belonging to respective feature point sets whose coordinatepositions substantively match one another remain in two feature pointsets. That is, the synthesizing part 302 extracts two feature pointpairs whose coordinate positions substantively match each other. Thesynthesizing part 302, by executing processing of step S102 andfollowing with regard to the feature point pairs, executes processing ofsynthesizing feature points where coordinate positions match (featurepoint synthesis processing).

First, the synthesizing part 302 confirms whether or not the types ofthe two feature points forming the feature point pair match (step S102).

If the types of the feature points match (step S102, Yes branch), thesynthesizing part 302 averages the coordinate positions and featurepoint directions of the two feature points (step S103). For example, thecoordinate positions and feature point directions of the two featurepoints (branch points) shown in FIGS. 11A and 11B are averaged. Thecoordinates of feature points shown in FIG. 11A are (X1a, Y1a), and thefeature point 520 direction is 01a. The coordinates of feature pointsshown in FIG. 11B are (X1b, Y1b), and the feature point direction is01b.

The synthesizing part 302 averages the coordinate positions of the twofeature points according to the following formula (1), and obtainscoordinates (Xs, Ys).

$\begin{matrix}{\left( {{Xs},{Ys}} \right) = \left( {\frac{{X1a} + {X1b}}{2},\frac{{Y\; 1a} + {Y\; 1b}}{2}} \right)} & (1)\end{matrix}$

Similarly, the synthesizing part 302 averages the 2 feature pointdirections according to the following formula (2), and obtains featurepoint direction θs.

$\begin{matrix}{{\theta s} = \frac{{\theta 1a} + {\theta 1b}}{2}} & (2)\end{matrix}$

The synthesizing part 302 stores the averaged feature point coordinatesand the feature point direction as feature points after synthesis in thestorage part 304.

In the judgment in step S102, if the types of the two feature points donot match (step S102, No branch), the synthesizing part 302 averages thecoordinate positions of the two feature points, and also sets thedirection and type of the feature points after synthesizing to “unknown”(step S104).

The synthesizing part 302 confirms whether or not processing of featurepoint pairs extracted by execution of step S101 is finished (step S105),and if not finished, processing of step S102 and following is repeated.

In the examples of FIGS. 8A-8C and FIGS. 9A and 9B, since feature point253 of FIG. 8B and feature point 263 of FIG. 8C have the same coordinatepositions, if the coordinates are averaged, (X3, Y3) is obtained. On theother hand, since the types of the feature points at the abovementionedtwo coordinates differ from one another, attributes of the two featurepoints (feature point direction and type) are cleared, and the featurepoint direction and type of the feature points after synthesis are setto “unknown”. The synthesizing part 302 sets the coordinates of theaveraged feature point to the coordinates of the feature point aftersynthesis, and also sets the feature point type and the feature pointdirection to “unknown”, to be stored in the storage part 304.

As described above, feature point synthesis processing executed by thesynthesizing part 302 includes averaging at least the coordinatepositions of feature points to be synthesized, irrespective of the typeof feature points to be synthesized.

If the synthesizing part 302 executes synthesis processing according toa logical OR operation with respect to the two feature point sets shownin FIGS. 8A-8C and FIGS. 9A and 9B, the result shown in FIG. 12A isobtained. It is to be noted that in FIG. 12A, feature point directionand type that have been set to unknown as described above, are denotedby a symbol “-”. The feature point direction θ26 of feature point 275 isan average value of feature point direction θ2 of feature point 252 andfeature point direction θ6 of feature point 262.

Reflecting the result shown in FIG. 12A in FIG. 8A which is an inputimage, FIG. 12B is obtained. It is to be noted that the feature pointwith type set as unknown (feature point 276) is denoted by a whitetriangle.

Continuing, a description is given concerning a case where a synthesismode instruction indicates a logical AND operation.

In this case also, a description is given taking as an example a casewhere a fingerprint image shown in FIG. 8A is inputted to the featurepoint extraction system, the first feature point extraction apparatus 10extracts the feature points shown in FIG. 8B, and the second featurepoint extraction apparatus 20 extracts the feature points shown in FIG.8C.

FIG. 13 is a flow chart showing an example of operations when thesynthesizing part 302 synthesizes two feature point sets by a logicalAND operation.

The synthesizing part 302 extracts pairs of feature points, respectivelyincluded in the two feature point sets, the feature points havingcoordinates that substantively match (overlap) (step S201). That is, ina case where the executed logical operation is a logical AND operation,by the synthesizing part 302 executing processing of step S201, featurepoint pairs, with feature points belonging to respective feature pointsets, that have coordinate positions substantively matching one anotherare selected. For example, in the example of FIGS. 8A-8C, the pair offeature point 252 and feature point 262, and the pair of feature point253 and feature point 263 are extracted.

Next, the synthesizing part 302 confirms whether or not the featurepoint types of the respective feature point pairs match (step S202).

If the feature point types match (step S202, Yes branch), thesynthesizing part 302 averages the coordinate positions and featurepoint directions (step S203). Since the processing in question may besimilar to the processing of step S103 of FIG. 10 , a detaileddescription is omitted.

The synthesizing part 302 stores averaged feature point coordinates andfeature point directions as feature points after synthesis, in thestorage part 304.

If the types of the feature points do not match (step S202, No branch),the synthesizing part 302 averages the coordinate positions of the twofeature points, and also sets the feature point directions and typeswith regard to the feature points after synthesizing to “unknown” (stepS204). Since the processing in question may be similar to the processingof step S104 of FIG. 10 , a detailed description is omitted.

The synthesizing part 302 sets the coordinates of the averaged featurepoints to the coordinates of the feature points after synthesis, andalso sets the feature point types and the feature point directions to“unknown”, to be stored in the storage part 304.

The synthesizing part 302 confirms whether or not processing of featurepoint pairs extracted in step S201 is finished (step S205), and if notfinished, processing of step S202 and following is repeated.

If the synthesizing part 302 executes synthesis processing according toa logical AND operation, with respect to the two feature point setsshown in FIGS. 8A-8C and FIGS. 9A and 9B, the result shown in FIG. 14Ais obtained. Reflecting the result shown in FIG. 14A in FIG. 8A which isan input image, FIG. 14B is obtained.

The synthesizing part 302 delivers feature point sets after synthesisobtained by the abovementioned synthesis processing (information asshown in FIG. 12 A or FIG. 14A) to the output part 303. The output part303 outputs the feature point set after synthesizing, as feature points(feature amount) corresponding to the fingerprint image inputted to thesystem, to an external apparatus. External apparatuses that are outputdestinations of the feature points in question relate to anauthentication apparatus that performs individual authentication, amatching apparatus to identify an individual stored in a database, andthe like.

Next, a description is given concerning operations of the feature pointextraction system according to the first example embodiment, makingreference to the drawings.

FIG. 15 is a sequence diagram illustrating an example of operations of afeature point extraction system according to the first exampleembodiment.

Fingerprint images are inputted to each of the first feature pointextraction apparatus 10 and the second feature point extractionapparatus 20. (steps S11, S21). Thereafter, after central lineextraction processing (steps S12, S22) and feature point extractionprocessing (step S13, S23), the first and second feature point sets areoutputted (steps S14, S24).

The synthesizing apparatus 30, for example, inputs a synthesis modeinstruction from an operator of the system (step S31). Thereafter, thesynthesizing apparatus 30 inputs two feature point sets (step S32), andexecutes synthesis processing according to the synthesis modeinstruction (step S33). The synthesizing apparatus 30 outputs thefeature point set after synthesizing, to an external apparatus (stepS34).

As described above, the synthesizing apparatus 30 according to the firstexample embodiment executes a logical operation (for example, logical ORoperation, logical AND operation) on the feature point sets extractedaccording to a plurality of methods (algorithms), and a plurality offeature point sets are synthesized. On this occasion, in a case wherefor feature points belonging to feature point sets extracted by aplurality of methods, the coordinate positions substantively match eachother, by executing predetermined synthesis processing (FIG. 10 , stepsS103, S104; FIG. 13 steps S203, S204) the synthesizing apparatus 30executes synthesizing of feature points. The synthesis mode may bedetermined by a synthesis mode instruction by the synthesizing apparatus30. As a result, a feature point set is obtained that matches usageobject of feature points extracted from the first feature pointextraction system.

For example, in a case where it is desired to comprehensively extractfeature points from a first fingerprint image, a logical OR operationmay be designated by the synthesis mode instruction. In this case, asshown in FIG. 12B, the feature points outputted by two feature pointextraction apparatuses are selected without omission. In regard to this,in a case where it is desired to extract feature points of high qualityfrom one fingerprint image, a logical AND operation may be designated bythe synthesis mode instruction. In this case, as shown in FIG. 14B,feature points extracted commonly by two feature point extractionapparatuses are selected. The feature points extracted by the respectivetwo apparatuses, that have different feature point extractionalgorithms, may be said to have features as feature points confirmedeven from any viewpoint, and they may be considered to be of highquality.

Second Example Embodiment

Next, a detailed description is given concerning a second exampleembodiment, making reference to the drawings.

There are various types of algorithm (feature point extractionalgorithm) used when extracting feature points from a fingerprint image.For example, there is an algorithm that can make a ridge a central linewith high accuracy if applied to an area with wide ridge width, and alsothere is an algorithm that can make a ridge a central line with highaccuracy if applied to an area with narrow ridge width. Also, in onefingerprint image, areas where ridge width is wide and areas where ridgewidth is narrow are often mixed.

For this type of fingerprint image, applying synthesis processingaccording to the first example embodiment, there is a possibility of notobtaining a feature point set with sufficiently high quality. Forexample, as shown in the upper section of FIG. 16 , a fingerprint imageis assumed where an area with wide ridges and an area with narrow ridgesare mixed. It is assumed that the first feature point extractionapparatus 10 executes an algorithm effective in an area where ridgewidth is wide (below, denoted algorithm A), and the second feature pointextraction apparatus 20 executes an algorithm effective in an area whereridge width is narrow (below, denoted algorithm B).

In this case, if algorithm A is applied to the fingerprint image shownin the upper section of FIG. 16 , the result of extracting an area witha wide ridge width is of high quality, but the result of extracting anarea with a narrow ridge width is of low quality (refer to themid-section of FIG. 16 , left side). Meanwhile, if algorithm B isapplied to the fingerprint image shown in the upper section of FIG. 16 ,opposite results are obtained (refer to the mid-section of FIG. 16 ,right side).

If a logical OR operation is executed by the synthesizing apparatus 30with respect to the two feature point sets shown in the mid-section ofFIG. 16 , feature points extracted by the two algorithms arecomprehensively extracted. However, in this case many feature pointsextracted by any one of the algorithms remain, and the quality of suchfeature points is not necessarily high. That is, in a case of overallconsideration of the feature point sets after synthesizing, it cannotnecessarily be said that high quality feature point sets are realized.

Similarly, if a logical AND operation is executed by the synthesizingapparatus 30 on a fingerprint image shown in the mid-section of FIG. 16, extraction is limited to feature points commonly extracted by the twoalgorithms. However in this case, there may be a possible occasion of acase where only feature points of very high quality remain, and a statemay occur where feature points of relatively high quality do not remain.In this case also, in a case of overall consideration of the featurepoint sets after synthesizing, it cannot necessarily be said that highquality feature point sets are generated.

Taking the above situation into account, in the second exampleembodiment a synthesizing apparatus 30 a is provided that extractsfeature points of overall high quality (feature point sets aftersynthesizing).

Specifically, the synthesizing apparatus 30 a according to the secondexample embodiment inputs three or more feature point sets, and byhierarchically executing synthesis processing a number of times, featurepoints of overall high quality are extracted.

FIG. 17 is a diagram illustrating an example of a feature pointextraction apparatus configuration according to the second exampleembodiment. In the system shown in FIG. 17 , a third feature pointextraction apparatus 40 is added.

The third feature point extraction apparatus 40 is an apparatus thatextracts a feature point from a fingerprint image, similar to the firstfeature point extraction apparatus 10 and the second feature pointextraction apparatus 20. Thus a description concerning configuration andoperations of the third feature point extraction apparatus 40 isomitted.

Feature points (a feature point set) extracted by the third featurepoint extraction apparatus 40 are denoted as a third feature point set.

Feature point extraction algorithms executed by the respective featurepoint extraction apparatuses are as follows. The first feature pointextraction apparatus 10 deals with algorithm A (effective when ridgewidth is wide). The second feature point extraction apparatus 20 dealswith algorithm B (effective when ridge width is narrow). The thirdfeature point extraction apparatus 40 deals with an algorithm C, and thealgorithm C has a feature (almighty or powerful quality) intermediatebetween algorithms A and B.

Since the basic configuration and operations of the synthesizingapparatus 30 a according to the second example embodiment may be thesame as content described in the first example embodiment, a detaileddescription is omitted. The synthesizing apparatus 30 a according to thesecond example embodiment differs from the synthesizing apparatus 30according to the first example embodiment in the point of dealing withthree or more feature point sets, and the point of executing synthesisprocessing multiple times.

A description is given below concerning the synthesizing part 302 a (notshown) of the synthesizing apparatus 30 a.

FIG. 18 is a flowchart showing an example of operations of thesynthesizing part 302 a according to the second example embodiment.

First, on obtaining three feature point sets the synthesizing part 302 aexecutes synthesizing with respect to a logical AND operation, for eachfeature point set pair (pair of feature point sets selected from aplurality of feature point sets) (step S301).

Next, the synthesizing part 302 a executes synthesizing with respect toa logical OR operation for each respective feature point set aftersynthesizing, obtained by executing the previous step (step S302).

When the synthesis processing according to the abovementioned stepsS301, S302 is represented by a calculation formula, the followingformula (3) applies.(Ag&&Bg)∥(Ag&&Cg)∥(Bg&&Cg)  (3)It is to be noted that Ag indicates a first feature point set (a setobtained according to algorithm A), Bg indicates a second feature pointset (a set obtained according to algorithm B), Cg indicates a thirdfeature point set (a set obtained according to algorithm C).Furthermore, “&&” denotes a “logical AND” operation.

According to the abovementioned formula (3), there remains only what iscommon in feature points obtained by two different algorithms (featurepoint set pairs), by synthesizing with regard to a first logical ANDoperation. That is, only good quality feature points common to the twoalgorithms are extracted. Thereafter, in formula (3), synthesizing withrespect to a logical OR operation is executed for feature point setsobtained by synthesizing according to a logical AND operation, and highquality feature points are collected. As a result, if synthesisprocessing according to formula (3) is executed, sets (feature pointsets after synthesizing) are generated that include high quality featurepoints, being feature points extracted according to algorithms A to C.

It is to be noted that the abovementioned description does not refer toa synthesis mode instruction, but execution of processing equivalent toformula (3) by the synthesizing apparatus 30 a may be predetermined, anda logical formula equivalent to formula (3) may be inputted as asynthesis mode instruction. Synthesis processing executed by thesynthesizing apparatus 30 a is not limited to formula (3), and forexample, synthesis processing according to formula (4) below,simplifying formula (3), may be executed.(Ag&&Cg)∥(Bg&&Cg)  (4)Formula (4) indicates performing a logical AND operation on featurepoint set pairs of each of algorithms A and C, and algorithms B and C,and thereafter, performing a logical OR operation on a result of thelogical AND operation. Furthermore, “&&” denotes a “logical AND”operation.

Here, confirming formula (3), if feature point extraction algorithmsapplied to fingerprint images are increased, and among feature pointsextracted by various algorithms only those of high quality arecollected, it is clear that higher quality feature point sets areobtained. However, in this case, due to problems such as processing timefor executing the respective algorithms, a problem may occur whereby along time is taken from inputting a fingerprint image to the system toextracting final feature points (feature point sets after synthesizing).Therefore, as a synthesis mode instruction, the synthesizing apparatus30 a may be configured to receive, for example, “highest quality mode”,“high quality mode”, and “low quality mode”, and content of synthesisprocessing or algorithm used in synthesis processing (combinations oflogical AND operation etc.) may be modified adapted for respectivemodes. For example, separate usage may be performed so that synthesisprocessing equivalent to formula (3) is executed in “highest qualitymode”, and synthesis processing equivalent to formula (4) is executed in“high quality mode”. As a result thereof, it is possible to realizebalance between processing time required for feature point extractionand quality of feature points obtained.

As described above, with the synthesizing apparatus 30 a according tothe second example embodiment, by executing hierarchical logicaloperations a number of times with regard to a plurality of feature pointsets, it is possible to extract feature points (feature point sets) ofoverall high quality.

Third Example Embodiment

Next, a detailed description is given concerning a third exampleembodiment, making reference to the drawings.

In the third example embodiment, a description is given concerning acase of outputting central line information corresponding to featurepoint sets after synthesizing, along with feature point sets, to anexternal apparatus.

FIG. 19 is a diagram illustrating an example of a feature pointextraction apparatus configuration according to the third exampleembodiment.

With regard to the first feature point extraction apparatus 10 a, afunction is added to output to the synthesizing apparatus 30 b a firstcentral line image used in extracting feature points in the firstfeature point extraction apparatus 10 described in the first exampleembodiment. Similarly, the second feature point extraction apparatus 20a has a function to output the second central line image to thesynthesizing apparatus 30 b. The synthesizing apparatus 30 b is furtherprovided with a function to generate central line information based on 2central line images. It is to be noted that central line information isinformation indicating the number of ridges between feature pointsbelonging to a feature point set after synthesizing.

FIG. 20 is a diagram illustrating an example of a processingconfiguration of the synthesizing apparatus 30 b. Referring to FIG. 20 ,the synthesizing apparatus 30 b is further provided with a central lineinformation generation part 305, in addition to the configuration of thesynthesizing apparatus 30 shown in FIG. 7 .

The central line information generation part 305 is a means forgenerating central line information related to feature point sets aftersynthesizing, based on a plurality of central line images respectivelycorresponding to a plurality of feature point sets. Specifically, thecentral line information generation part 305 reflects the feature pointsets after synthesizing in respective central line images. It is to benoted that reflecting the feature point sets after synthesizing thecentral line images relates to obtaining coordinate positions of featurepoints belonging to the feature point sets after synthesizing, andsetting feature points at a place corresponding to the coordinatepositions in the central line images.

Next, the central line information generation part 305 counts the numberof ridges between feature points in the central line image reflectingthe feature points. Thereafter, the central line information generationpart 305 generates as central line information an average value of thenumber of ridges between feature points counted from respective centralline images.

For example, a central line image shown in FIG. 21A as a first centralline image, is obtained from the first feature point extractionapparatus 10 a. Feature points 291 to 294 after synthesizing that havebeen generated by synthesis processing are reflected in FIG. 21A. Inthis case, for example, the number of ridges between feature point 291and feature point 292 is calculated as “2”. Similarly, the number ofridges between feature point 292 and feature point 294 is calculated as“4”.

The central line information generation part 305 reflects feature pointsets after synthesizing, in a second central line image, and calculatesthe number of ridges between feature points obtained from the diagram inquestion. For example, a central line image shown in FIG. 21B isobtained as the second central line image, and if some of the number ofridges between the central lines are counted, the situation is as inFIG. 21B.

The central line information generation part 305 calculates the averagevalue of the number of ridges between the feature points correspondingto the two central lime images respectively, as central line informationcorresponding to feature point sets after synthesizing. For example,since the number of ridges of feature point 291 and feature point 292 inthe first central line image is “2”, and the number of ridges betweenfeature point 291 and feature point 292 in the second central line imageis “1”, the average value of the number of ridges between feature point291 and feature point 292 image is “1.5”.

The central line information generation part 305 applies theabovementioned processing of calculating the number of ridges betweenfeature points and processing of calculating the average value of thenumber of ridges between feature points, to all feature points, andgenerates central line information. The generated central lineinformation is outputted to an external apparatus via the output part303. In the external apparatus, by handling the central line informationas a feature amount characterizing a fingerprint image, usage ispossible for individual authentication or individual identification.

As described above, in the third example embodiment, in addition to afeature point set after synthesizing, it is possible to output thenumber of ridges between feature points corresponding to the featurepoint set in question, as central line information, to the externalapparatus. As a result, it is possible to improve accuracy of matchingprocessing using the information in question.

Application Example

Next, a description is given concerning practical use of feature pointsobtained by synthesizing apparatuses according to the first to thirdexample embodiments.

For example, it is possible to build a matching system according tofeature point sets outputted by a synthesizing apparatus according tothe first to third example embodiments. For example, high qualityfeature points are extracted by the synthesizing apparatus 30 aaccording to the second example embodiment and recorded in a database.Feature points are extracted by the synthesizing apparatus 30 a from afingerprint image that is a target for matching, and used as matchingdata. In this case, it is possible to perform matching processing usinghigh quality feature points a small number of times. In the example ofthe abovementioned second example embodiment, three algorithms A to Care used, and one feature point set is generated. If there is anintention to obtain the same level of accuracy as in matching using afeature point set after synthesizing, matching processing by respectivealgorithms A to C must be done three times. In contrast, in the secondexample embodiment, since high quality feature points according to thethree algorithms A to C are integrated as one feature point set, it issufficient to execute matching processing once.

The feature point set obtained by a logical AND operation in the firstexample embodiment can be used in synthesizing a plurality offingerprint images. Specifically, for each fingerprint image, featurepoint sets are extracted by a logical AND operation described in thefirst example embodiment. Thereafter, areas are calculated with commonfeature points among the extracted feature point sets. The feature pointsets obtained by the logical AND operation are of high quality, andareas in which such high quality feature points exist in common may betaken as common portions of a fingerprint image obtained from the sameindividual. Therefore, with regard to a plurality of fingerprint images,by image modification to have common areas overlap, it is possible tosynthesize one fingerprint image from a plurality of images.

Modified Example

It is to be noted that the configuration of the feature point extractionsystem described in the first to third example embodiments (FIG. 2 ,FIG. 17 , FIG. 19 ) is exemplary, and there is no intention to limit theconfiguration of the system. For example, functions of the feature pointextraction apparatus may be built into a synthesizing apparatus.

Or, a feature point extraction apparatus need not be included in thefeature point extraction system. For example, feature point setsextracted from the same fingerprint image by a plurality of examinersmay be inputted to the synthesizing apparatus 30. In this case also, thesynthesizing apparatus 30 synthesizes a plurality of feature point setsby a method described in the first to third example embodiments, and itis possible to generate one feature point set.

A feature point set that is a target of the synthesizing apparatus 30may be extracted by any method. As described above, manual extraction ofa plurality of feature point sets from a fingerprint image by anexaminer may be a target for synthesis, or there may be a mixture ofmanually extracted feature point sets and feature point sets extractedautomatically by an apparatus. Or, the result of an examiner correctinga feature point, which has been automatically extracted by an apparatus,may be used as a feature point set. That is, feature point setsextracted by different methods or algorithms may be targets forsynthesizing by the synthesizing apparatus 30.

In the first example embodiment a description was given of a case ofsynthesizing two feature point sets, but the number of synthesizedfeature point sets is not limited to two. Clearly it is possible forthree or more feature point sets to be targets for synthesizing. Byincreasing the feature point sets that are targets for synthesizing, itis possible to improve requirements for a feature point set, such asfeature point completeness or accuracy.

In the abovementioned example embodiments a description was givenconcerning cases of inputting fingerprint images, but images that can behandled by the feature point extraction system are not limited tofingerprint images. An image formed by curved stripes of ridges such asa palm print or the like is also possible.

In the abovementioned example embodiments, a description was given wherefeature points belonging to a feature point set are provided withattributes such as feature point direction or type, but feature pointsbelonging to the feature point set need not be provided with theseattributes. The feature point set may be simply a collection of featurepoints.

In multiple flowcharts that use the abovementioned description, aplurality of steps (processes) were described in order, but the order ofexecuting the steps executed in the respective example embodiments isnot limited to the order described. In the various example embodiments,modification of the order of the illustrated steps is possible within ascope that does not interfere with content, such as executing therespective processes in parallel. The various example embodimentsdescribed above may be combined within a scope that does not conflictwith the content.

It is possible to have a computer function as a synthesizing apparatusby installing the abovementioned computer program in the computerstorage part. In addition, by causing the abovementioned computerprogram to execute a computer, it is possible to execute a synthesizingmethod for feature point sets by the computer.

Some of all of the abovementioned example embodiments may also bedescribed as in the following, but there is no limitation thereto.

<Mode 1>

As in the synthesizing apparatus according to the first aspect described1045 above.

<Mode 2>

The synthesizing apparatus preferably according to the mode 1, whereinthe synthesizing part executes a logical OR operation on the pluralityof feature point sets.

<Mode 3>

The synthesizing apparatus preferably according to mode 1 or 2, whereinthe synthesizing part executes a logical AND operation on the pluralityof feature point sets.

<Mode 4>

The synthesizing apparatus preferably according to any one of modes 1 to3, wherein the synthesizing part executes at least 2 logical operationson the plurality of feature point sets.

<Mode 5>

The synthesizing apparatus preferably according to mode 4, wherein thesynthesizing part hierarchically executes at least 2 logical operations.

<Mode 6>

The synthesizing apparatus preferably according to any one of modes 1 to5, wherein the synthesizing part, for feature points respectivelybelonging to the plurality of feature point sets, in a case ofcoordinate positions substantively matching each other, performsprocessing to synthesize the feature points whose coordinate positionsmatch, and the processing to synthesize the feature points includesaveraging coordinate positions of at least the feature points to besynthesized.

<Mode 7>

The synthesizing apparatus preferably according to mode 6, wherein theprocessing to synthesize the feature points further includes processingto average feature point directions characterizing the direction of thefeature points whose coordinate positions match.

<Mode 8>

The synthesizing apparatus preferably according to mode 6 or 7, whereinthe processing to synthesize the feature points further includesprocessing to set the type of feature points after synthesizing asunknown, in a case where types differ for the feature points whosecoordinate positions match.

<Mode 9>

The synthesizing apparatus preferably according to any one of modes 1 to8, wherein the synthesizing part executes a logical AND operation on apair of feature point sets selected from the plurality of feature pointsets, and executes a logical OR operation on feature point sets afterexecution of the logical AND operation.

<Mode 10>

The synthesizing apparatus preferably according to any one of modes 1 to9, further comprising a central line information generation part thatgenerates central line information related to the feature point setsafter synthesizing, based on a plurality of central line imagescorresponding respectively to the plurality of feature point sets.

<Mode 11>

The synthesizing apparatus preferably according to mode 10, wherein thecentral line information generation part reflects feature pointsbelonging to the feature point sets after synthesizing, respectively inthe plurality of central line images, counts the number of ridgesbetween feature points in the central line images reflecting the featurepoints, and generates, as the central line information, an average valueof the number of ridges between the feature points counted respectivelyfrom the plurality of central line images.

<Mode 12>

As in the synthesizing method according to the second aspect describedabove.

<Mode 13>

As in the program according to the third aspect described above.

It is to be noted that mode 12 and mode 13 may be extended with regardto the mode 1 to mode 11, similar to mode 1.

It is to be noted that the respective disclosures of the cited PatentLiterature described above are incorporated herein by reference thereto.Modifications and adjustments of example embodiments and examples may bemade within the bounds of the entire disclosure (including the scope ofthe claims) of the present invention, and also based on fundamentaltechnological concepts thereof. Various combinations and selections ofvarious disclosed elements (including respective elements of therespective claims, respective elements of the respective exampleembodiments and examples, respective elements of the respectivedrawings, and the like) are possible within the scope of the entiredisclosure of the present invention. That is, the present inventionclearly includes every type of transformation and modification that aperson skilled in the art can realize according to the entire disclosureincluding the scope of the claims and to technological concepts thereof.In particular, with regard to numerical ranges described in the presentspecification, arbitrary numerical values and small ranges included inthe relevant ranges should be interpreted to be specifically describedeven where there is no particular description thereof.

REFERENCE SIGNS LIST

-   -   10, 10 a first feature point extraction apparatus    -   11 CPU (Central Processing Unit)    -   12 memory    -   13 input output interface    -   14 NIC (Network Interface Card)    -   20, 20 a second feature point extraction apparatus    -   30, 30 a, 30 b, 100 synthesizing apparatus    -   40 third feature point extraction apparatus    -   101, 201, 301 input part    -   102, 302, 302 a synthesizing part    -   202 central line extraction part    -   203 feature point extraction part    -   204, 303 output part    -   205, 304 storage part    -   211-222, 231, 241, 251-254, 261-264, 271-276, 281, 282, 291-294        feature points    -   232, 242 feature point direction    -   305 central line information generation part

The invention claimed is:
 1. A synthesizing apparatus, comprising: atleast one memory storing instructions; and at least one processorconfigured to execute the instructions to: receive an input image of afingerprint; input a plurality of feature point sets extracted from theinput image, wherein the fingerprint in the input image has a curvedstripes pattern formed by ridges, and the plurality of feature pointsets include at least one information on type, position and direction;synthesize the plurality of feature point sets into one synthesizedfeature point set about at least one information on type, position anddirection, wherein the one synthesized feature point set is synthesizedby synthesizing feature points into one or more synthesized featurepoints by averaging coordinate positions of the feature points, whereinthe feature points respectively belong to the plurality of feature pointsets and coordinate positions of the feature points substantially matcheach other.
 2. The synthesizing apparatus according to 1, wherein theprocessor is configured to execute a logical operation on the pluralityof feature point sets.
 3. The synthesizing apparatus according to 2,wherein the processor is configured to execute a logical OR operation onthe plurality of feature point sets.
 4. The synthesizing apparatusaccording to 2, wherein the processor is configured to execute a logicalAND operation on the plurality of feature point sets.
 5. Thesynthesizing apparatus according to 2, wherein the processor isconfigured to execute at least two or more logical operations on theplurality of feature point sets.
 6. The synthesizing apparatus accordingto 5, wherein the processor is configured to hierarchically execute atleast two or more logical operations.
 7. The synthesizing apparatusaccording to 1, wherein the feature points are synthesized into the oneor more synthesized feature points by averaging feature point directionscharacterizing the directions of the feature points having coordinatepositions that match each other.
 8. The synthesizing apparatus accordingto 1, wherein the processor is configured to set a type of the one ormore synthesized feature points as unknown, in a case where types of thefeature points having coordinate positions that match each other aredifferent from one another.
 9. The synthesizing apparatus according to1, wherein the processor is configured to execute a logical OR operationand a subsequent logical AND operation on a pair of feature pointsselected from the plurality of feature point sets.
 10. The synthesizingapparatus according to claim 1, wherein the processor is furtherconfigured to generate central line information related to the one ormore synthesized feature points, based on a plurality of central lineimages corresponding respectively to the plurality of feature pointsets.
 11. The synthesizing apparatus according to 10, wherein theprocessor is configured to reflect the one or more synthesized featurepoints belonging to the one synthesized feature point set respectivelyto the plurality of central line images, count a number of the ridgesbetween a first synthesized feature point and a second synthesizedfeature point in the central line images in which the one or moresynthesized feature points are reflected, and generate, as the centralline information, an average value of the number of ridges between thefirst synthesized feature point and the second synthesized feature pointcounted respectively from the plurality of central line images.
 12. Asynthesizing method comprising: receiving, by a processor, an inputimage of a fingerprint; inputting, by the processor, a plurality offeature point sets extracted from the input image, wherein thefingerprint in the input image has a curved stripes pattern formed byridges, and the plurality of feature point sets include at least oneinformation on type, position and direction; synthesizing, by theprocessor, the plurality of feature point sets into one synthesizedfeature point set about at least one information on type, position anddirection, wherein the one synthesized feature point set is synthesizedby synthesizing feature points into one or more synthesized featurepoints by averaging coordinate positions of the feature points, whereinthe feature points respectively belong to the plurality of feature pointsets and coordinate positions of the feature points substantially matcheach other.
 13. A non-transitory computer-readable recording mediumstoring thereon a program that when executed by a computer causes thecomputer to execute processing comprising: receiving an input image of afingerprint; inputting a plurality of feature point sets extracted fromthe input image, wherein the fingerprint in the input image has a curvedstripes pattern formed by ridges, and the plurality of feature pointsets which include at least one information on type, position anddirection; synthesizing the plurality of feature point sets into onesynthesized feature point set about at least one information on type,position and direction, wherein the one synthesized feature point set issynthesized by synthesizing feature points into one or more synthesizedfeature points by averaging coordinate positions of the feature points,wherein the feature points respectively belong to the plurality offeature point sets and coordinate positions of the feature pointssubstantially match each other.